Seed Randomness for Repeatable Runs
Control random seeds across NumPy, PyTorch, and Python.
Why Runs Differ
ML uses randomness for shuffling, splits, and weight init. Run twice and you get two different models. Seeding makes randomness repeatable. 🎲
What a Seed Is
A seed is a starting number for the random generator. Same seed in means the same sequence of random numbers out.
All lessons in this course
- Pin Dependencies with requirements.txt
- Isolate Projects with Virtual Environments
- Seed Randomness for Repeatable Runs
- Capture the Full Run Config